Reactor loop fouling monitor for rotating equipment in a petrochemical plant or refinery

ABSTRACT

A plant or refinery may include equipment such as condensers, regenerators, distillation columns, rotating equipment, compressors, pumps, turbines, or the like. Different operating methods may impact deterioration in equipment condition, thereby prolonging equipment life, extending production operating time, or providing other benefits. Mechanical or digital sensors may be used for monitoring equipment to determine whether problems are developing. For example, sensors may be used in conjunction with one or more system components to perform invariant mapping, monitor system operating characteristics, and/or predict pressure, volume, surges, reactor loop fouling, gas quality, or the like. An operating condition (e.g., of one or more pieces of equipment in the plant or refinery) may be adjusted to prolong equipment life or avoid equipment failure.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119(e)of U.S. Provisional Application No. 62/477,876, filed Mar. 28, 2017,which is incorporated by reference in its entirety.

FIELD

The present disclosure is related to a method and system for improvingthe performance of components that make up operations in a plant, suchas a carbonaceous processing plant, a chemical plant, a petrochemicalplant, or a refinery. Typical plants may be those that provide catalyticdehydrogenation or hydrocarbon cracking.

BACKGROUND

A plant or refinery may include one or more pieces of equipment forperforming a process. Equipment may break down over time, and need to berepaired or replaced. Additionally, a process may be more or lessefficient depending on one or more operating characteristics. There willalways be a need for improving process efficiencies and improvingequipment reliability.

SUMMARY

The following summary presents a simplified summary of certain features.The summary is not an extensive overview and is not intended to identifykey or critical elements.

One or more embodiments may include a system that includes a reactor; aheater; a compressor comprising one or more injection ports; one or moresensors associated with the compressor; a data collection platform,and/or a data analysis platform. The data collection platform mayinclude one or more processors of the data collection platform; acommunication interface of the data collection platform; andcomputer-readable memory storing executable instructions that, whenexecuted, cause the data analysis platform to: receive, from the one ormore sensors associated with the compressor, sensor data associated withthe compressor and collected by the one or more sensors associated withthe compressor; and send the sensor data associated with the compressorand collected by the one or more sensors associated with the compressor.The data analysis platform may include one or more processors of thedata analysis platform; a communication interface of the data analysisplatform; and computer-readable memory storing executable instructionsthat, when executed, cause the data analysis platform to: receive thesensor data associated with the compressor and collected by the one ormore sensors associated with the compressor; analyze the sensor dataassociated with the compressor to determine potential fouling within thecompressor; and based on determining the potential fouling within thecompressor, send a command configured to cause an online wash via theone or more injection ports of the compressor to reduce the potentialfouling within the compressor.

One or more embodiments may include one or more non-transitorycomputer-readable media storing executable instructions that, whenexecuted, cause a data analysis platform to: receive sensor dataassociated with a compressor comprising one or more injection ports andcollected by one or more sensors associated with the compressor; analyzethe sensor data associated with the compressor to determine potentialfouling within the compressor; and based on determining the potentialfouling within the compressor, send a command configured to cause anonline wash via the one or more injection ports of the compressor toreduce the potential fouling within the compressor.

One or more embodiments may include a method including receiving, by adata analysis computing device, sensor data associated with a compressorcomprising one or more injection ports and collected by one or moresensors associated with the compressor; analyzing, by the data analysiscomputing device, the sensor data associated with the compressor todetermine potential fouling within the compressor; and based ondetermining the potential fouling within the compressor, sending, by thedata analysis computing device, a command configured to cause an onlinewash via the one or more injection ports of the compressor to reduce thepotential fouling within the compressor.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1A depicts an illustrative arrangement for a catalyticdehydrogenation process in accordance with one or more exampleembodiments;

FIG. 1B depicts an illustrative arrangement for a fluid catalyticcracking process in accordance with one or more example embodiments;

FIG. 2 depicts an illustrative arrangement for a centrifugal compressorconfigured for use in connection with the arrangement of FIGS. 1A and/or1B in accordance with one or more example embodiments;

FIG. 3 depicts an illustrative arrangement for an axial compressorconfigured for use in connection with the arrangement of FIGS. 1A and/or1B in accordance with one or more example embodiments;

FIG. 4 depicts a graph illustrating typical performance operation of acentrifugal or axial compressor in accordance with one or more exampleembodiments;

FIG. 5A depicts an illustrative arrangement for a portion of areciprocating compressor configured for use in connection with thearrangement of FIGS. 1A and/or 1B in accordance with one or more exampleembodiments;

FIG. 5B depicts an illustrative arrangement for a reciprocatingcompressor configured for use in connection with the arrangement ofFIGS. 1A and/or 1B in accordance with one or more example embodiments;

FIG. 6 depicts an illustrative arrangement for a steam turbineconfigured for use in connection with the arrangement of FIGS. 1A and/or1B in accordance with one or more example embodiments;

FIGS. 7-8 are intentionally omitted;

FIG. 9A depicts an illustrative arrangement for a centrifugal compressorwith pressure, temperature, and vibration sensors configured for use inconnection with the arrangement of FIG. 1 in accordance with one or moreexample embodiments;

FIG. 9B depicts an illustrative arrangement for a centrifugal compressorwith wash injection ports configured for use in connection with thearrangement of FIGS. 1A and/or 1B in accordance with one or more exampleembodiments;

FIG. 10 is intentionally omitted;

FIG. 11A depicts an illustrative computing environment for managing theoperation of one or more pieces of equipment in a plant in accordancewith one or more example embodiments;

FIG. 11B depicts an illustrative data collection computing platform forcollecting data related to the operation of one or more pieces ofequipment in a plant in accordance with one or more example embodiments;

FIG. 11C depicts an illustrative data analysis computing platform foranalyzing data related to the operation of one or more pieces ofequipment in a plant in accordance with one or more example embodiments;

FIG. 11D depicts an illustrative data analysis computing platform foranalyzing data related to the operation of one or more pieces ofequipment in a plant in accordance with one or more example embodiments;

FIG. 11E depicts an illustrative control computing platform forcontrolling one or more parts of one or more pieces of equipment in aplant in accordance with one or more example embodiments;

FIGS. 12A-12B depict an illustrative flow diagram of one or more stepsthat one more devices may perform in controlling one or more aspects ofa plant operation in accordance with one or more example embodiments;

FIGS. 13-14 depict illustrative graphical user interfaces related to oneor more aspects of a plant operation in accordance with one or moreexample embodiments; and

FIG. 15 depicts an illustrative flowchart of a process that one or moredevices may perform in controlling one or more aspects of a plantoperation in accordance with one or more example embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be used, and structural andfunctional modifications may be made, without departing from the scopeof the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

A chemical plant or a petrochemical plant or a refinery may include oneor more pieces of equipment that process one or more input chemicals tocreate one or more products. For example, catalytic dehydrogenation canbe used to convert paraffins to the corresponding olefin, e.g., propaneto propene, or butane to butene.

A multitude of process equipment may be used in the chemical, refining,and petrochemical industry including, but not limited to, slide valves,rotating equipment, pumps, compressors, heat exchangers, fired heaters,control valves, fractionation columns, reactors, and/or shut-off valves.

Elements of chemical and petrochemical/refinery plants may be exposed tothe outside and thus can be exposed to various environmental stresses.Such stresses may be weather related, such as temperature extremes (hotand cold), high-wind conditions, and precipitation conditions such assnow, ice, and rain. Other environmental conditions may be pollutionparticulates, such as dust and pollen, or salt if located near an ocean,for example. Such stresses can affect the performance and lifetime ofequipment in the plants. Different locations may have differentenvironmental stresses. For example, a refinery in Texas may havedifferent stresses than a chemical plant in Montana.

Process equipment may deteriorate over time, affecting the performanceand integrity of the process. Such deteriorating equipment mayultimately fail, but before failing, may decrease efficiency, yield,and/or product properties. It is desirable that corrective actions betaken in advance of equipment inefficiencies and/or failure.

FIG. 1A shows one typical arrangement for a catalytic dehydrogenationprocess 5. The process 5 includes a reactor section 10, a catalystregeneration section 15, and a product recovery section 20.

The reactor section 10 includes one or more reactors 25. A hydrocarbonfeed 30 is sent to a heat exchanger 35 where it exchanges heat with areactor effluent 40 to raise the feed temperature. The feed 30 is sentto a preheater 45 where it is heated to the desired inlet temperature.The preheated feed 50 is sent from the preheater 45 to the first reactor25. Because the dehydrogenation reaction is endothermic, the temperatureof the effluent 55 from the first reactor 25 is less than thetemperature of the preheated feed 50. The effluent 55 is sent tointerstage heaters 60 to raise the temperature to the desired inlettemperature for the next reactor 25.

After the last reactor, the reactor effluent 40 is sent to the heatexchanger 35, and heat is exchanged with the feed 30. The reactoreffluent 40 is then sent to the product recovery section 20. Thecatalyst 65 moves through the series of reactors 25. When the catalyst70 leaves the last reactor 25, it is sent to the catalyst regenerationsection 15. The catalyst regeneration section 15 includes a regenerator75 where coke on the catalyst is burned off and the catalyst may gothrough a reconditioning step. A regenerated catalyst 80 is sent back tothe first reactor 25.

The reactor effluent 40 is compressed in the compressor or centrifugalcompressor 82. The compressed effluent 115 is introduced to a cooler120, for instance a heat exchanger. The cooler 120 lowers thetemperature of the compressed effluent. The cooled effluent 125 (cooledproduct stream) is then introduced into a chloride remover 130, such asa chloride scavenging guard bed. The chloride remover 130 includes anadsorbent, which adsorbs chlorides from the cooled effluent 125 andprovides a treated effluent 135. Treated effluent 135 is introduced to adrier 84.

The dried effluent is separated in separator 85. Gas 90 is expanded inexpander 95 and separated into a recycle hydrogen stream 100 and a netseparator gas stream 105. A liquid stream 110, which includes the olefinproduct and unconverted paraffin, is sent for further processing, wherethe desired olefin product is recovered and the unconverted paraffin isrecycled to the dehydrogenation reactor 25.

FIG. 1B shows a typical fluid catalytic cracking (FCC) process, whichincludes an FCC fluidized bed reactor and a spent catalyst regenerator.Regenerated cracking catalyst entering the reactor, from the spentcatalyst regenerator, is contacted with an FCC feed stream in a risersection at the bottom of the FCC reactor, to catalytically crack the FCCfeed stream and provide a product gas stream, comprising crackedhydrocarbons having a reduced molecular weight, on average, relative tothe average molecular weight of feed hydrocarbons in the FCC feedstream. As shown in FIG. 1B, steam and lift gas are used as carriergases that upwardly entrain the regenerated catalyst in the risersection, as it contacts the FCC feed. In this riser section, heat fromthe catalyst vaporizes the FCC feed stream, and contact between thecatalyst and the FCC feed causes cracking of this feed to lowermolecular weight hydrocarbons, as both the catalyst and feed aretransferred up the riser and into the reactor vessel. A product gasstream comprising the cracked (e.g., lower molecular weight)hydrocarbons may be separated from spent cracking catalyst at or nearthe top of the reactor vessel, preferably using internal solid/vaporseparation equipment, such as cyclone separators. This product gasstream, essentially free of spent cracking catalyst, then exits thereactor vessel through a product outlet line for further transport tothe downstream product recovery section.

The spent or coked catalyst, following its disengagement or separationfrom the product gas stream, requires regeneration for further use. Thiscoked catalyst first falls into a dense bed stripping section of the FCCreactor, into which steam is injected, through a nozzle and distributor,to purge any residual hydrocarbon vapors that would be detrimental tothe operation of the regenerator. After this purging or strippingoperation, the coked catalyst is fed by gravity to the catalystregenerator through a spent catalyst standpipe. FIG. 1B depicts aregenerator, which can also be referred to as a combustor. Variousconfigurations of regenerators may be used. In the spent catalystregenerator, a stream of oxygen-containing gas, such as air, isintroduced to contact the coked catalyst, burn coke deposited thereon,and provide regenerated catalyst, having most or all of its initial cokecontent converted to combustion products, including CO2, CO, and H2Ovapors that exit in a flue gas stream. The regenerator operates withcatalyst and the oxygen-containing gas (e.g., air) flowing upwardlytogether in a combustor riser that is located within the catalystregenerator. At or near the top of the regenerator, following combustionof the catalyst coke, regenerated cracking catalyst is separated fromthe flue gas using internal solid/vapor separation equipment (e.g.,cyclones) to promote efficient disengagement between the solid and vaporphases.

In the FCC recovery section, the product gas stream exiting the FCCreactor is fed to a bottom section of an FCC main fractionation column.Several product fractions may be separated on the basis of theirrelative volatilities and recovered from this main fractionation column.Representative product fractions include, for example, naphtha (or FCCgasoline), light cycle oil, and heavy cycle oil.

Other petrochemical processes produce desirable products, such asturbine fuel, diesel fuel and other products referred to as middledistillates, as well as lower boiling hydrocarbonaceous liquids, such asnaphtha and gasoline, by hydrocracking a hydrocarbon feedstock derivedfrom crude oil or heavy fractions thereof. Feedstocks most oftensubjected to hydrocracking are the gas oils and heavy gas oils recoveredfrom crude oil by distillation.

References herein to a “plant” are to be understood to refer to any ofvarious types of chemical and petrochemical manufacturing or refiningfacilities. References herein to a plant “operators” are to beunderstood to refer to and/or include, without limitation, plantplanners, managers, engineers, technicians, operators, and othersinterested in, overseeing, and/or running the daily operations at aplant.

Rotating Equipment Technology

A system or arrangement as described above may include variouscompressors, pumps, and/or turbines, and FIGS. 1A and 1B illustrateexample locations for some of such components, which may be additionallyor alternately used in other locations. Compressors may be used tocompress gases within the system (e.g., a reactor effluent) to provide acompressed gas. Compression includes increasing a pressure of the gasand may also change other properties such as temperature. Pumps may beused to force fluids through the system. Centrifugal pumps are anexample of a frequently used pump in a plant as described herein.Turbines may be used for harnessing heat energy generated by the plant,such as to convert the heat energy into electrical energy and/or topower fans or other rotating equipment. Steam turbines, for example, areoften used in a plant as described herein.

There are several types of compressors typically used in chemical andpetrochemical plants and refineries, the most common of which arecentrifugal compressors, axial compressors, and reciprocatingcompressors. Many compressors in a plant as described herein arearranged in parallel with a redundant backup compressor, which can beactivated to prevent total shutdown when the original compressor needsto be taken offline. Centrifugal and axial compressors are dynamiccompressors that operate by transferring energy from a set of rotatingimpeller blades to a gas, which is then converted into potential energyin the form of increased gas pressure by diffusers that slow the flow ofthe gas, creating a pressurized output gas. FIG. 2 illustrates anexample of a centrifugal compressor 310, which includes an inlet orintake 311, a plurality of impellers 313 mounted on a rotatable shaft318 and located downstream from the inlet 311, and a plurality ofdiffusers 316 each located following one of the impellers 313, and anoutlet or discharge 317 at the far downstream end of the compressor 310.The compressor 310 operates by building pressure incrementally andsequentially in the diffusers 316. FIG. 3 illustrates an example of anaxial compressor 320, which includes an inlet or intake 321 a pluralityof impellers 323 mounted on a rotatable shaft 328 and located downstreamfrom the inlet 321, a stator 324 that has a plurality of stator vanes325 arranged in circumferential rings each located downstream from oneof the impellers 323 and operates as a diffuser 326, and an outlet ordischarge 327 at the far downstream end of the compressor 320.

Centrifugal or axial compressors may be referred to as dynamiccompressors or turbomachinery. Such compressors often have othercomponents immediately upstream and downstream that enhance or enablethe functioning of the compressor. Examples of such equipment includeisolation valves, a suction strainers, a compressor suction drum orseparator, an anti-surge spillback takeoff, a feed mix node and combinedfeed exchanger for H2 recycle, and an interstage drum or knockout drum.

Performance of all types of compressors may be affected by changes ingas conditions, including gas temperature and the composition and/ormolecular weight of the gas, among other factors. Process control ofcapacity may be made by speed variation, suction throttling, or variableinlet guide vanes. Compressors can be put through a variety of extremeconditions, such as high temperatures and pressures and corrosive andaggressive components.

Surge is a common issue faced by all centrifugal and axial compressors.Surge occurs when the outlet or discharge pressure of the compressor isequal to or greater than the pressure generated by the impellers 313,323. In a centrifugal or axial compressor, this phenomenon typicallyoccurs within the final diffuser 316, 326 before the outlet 317, 327.When this occurs, the increased outlet pressure drives airflowtemporarily backward toward the impeller or impellers 313, 323. Surgetypically happens in an oscillatory manner and is often accompanied byrapid (even exponential) temperature increase. Various factors can causesurging, such as increased discharge pressure, improper valve cycling,change in gas composition (e.g., decreased molecular weight of the gas),ramping the feed rate too fast, improper limit stop set point on thevalves, and other operational errors or malfunctions, among otherfactors. Surge can decrease the effectiveness and efficiency of thecompressor, and the vibrations, thrust reversals, and temperatureincreases that result from surging can damage components of thecompressor (sometimes quickly) and reduce the functional life of thecompressor. For example, vibrations and thrust reversal can cause damageto bearings and seals, and potentially cause contact between rotatingand stationary parts. As another example, temperature increases cancause damage to seals, thermal expansion of the rotor/impeller, andcontact between rotating and stationary parts.

Each dynamic compressor has a surge limit that represents a limit onoperation of the compressor. FIG. 4 illustrates the performanceoperating of a centrifugal compressor, plotting change in pressure (ΔP)against flow, with the surge limit forming a performance limitation ofthe compressor. Normal operation occurs between the lines defined by themaximum and minimum compressor speeds and to the right of the surgeline, and surge occurs when the surge line is crossed. An axialcompressor may perform in a similar manner.

Additional issues faced by centrifugal or axial compressors includebearing and seal failures, wear, fouling, and damage from contactbetween moving and non-moving components, among others. Such failuresmay be caused by vibrations, thrust reversals, excessive temperature,and unwanted chemicals in the feed gas. Some of these issues maydirectly or indirectly result from surging, but these issues may resultfrom other causes as well, including other causes described elsewhereherein.

A reciprocating compressor is a positive-displacement compressor thatoperates by a moveable member, e.g., a piston and/or amembrane/diaphragm, moving to decrease the volume of a cylinder filledwith a gas, thereby compressing the gas within the cylinder. FIG. 5Aillustrates an example of a piston-type reciprocating compressor 330,which includes a cylinder 331 with an inlet or intake 332 and an outletor discharge 333, with a piston 334 that reciprocates to draw gases inthrough the inlet 332 and compress gases contained in the cylinder 331.The inlet 332 and the outlet 333 may further have inlet and outletvalves to permit intake of gas through the inlet 332 and discharge ofcompressed gas through the outlet 333 at appropriate times. The openingand closing of such valves is critical to the operation of thecompressor. The valves should operate smoothly and timely as well asfully open and close at appropriate times. Vibrations and contaminantscan affect the performance and integrity of the valves and theiroperation The compressor 330 illustrated in FIG. 5B also includes apiston rod, a crosshead connected to the piston rod that rides within acrosshead guide, a crosshead pin, rider rings on the piston 334, a headend or cylinder end (CE) head, a pressure packing case, and a piston roddrop transducer. Many reciprocating compressors may include multiplecylinders 331 as shown in FIG. 5A, and the inlets in such an embodimentmay be provided through an intake manifold or suction manifold. FIG. 5Aalso illustrates the valve heads 338 of the compressor 330. Thepiston(s) 334 in a reciprocating compressor 330 may be driven by acrankshaft 337 in one or more embodiments.

Reciprocating compressors often have other components immediatelyupstream and downstream that enhance or enable the functioning of thecompressor. Examples of such equipment include an isolation valve, asuction strainer, an interstage cooler or aftercooler, and a dischargedrum.

One issue facing reciprocating compressors is ingress of liquidcontaminants, which may occur through a variety of mechanisms, such asimproper separation between gas and liquid components at some pointalong the line, seal leakage, condensation caused by insufficienttemperature at some point along the line and compounded by poor suctionpipe layout. Liquids are incompressible, and therefore, ingress ofliquids into the compressor can negatively affect operation of thecompressor. Other contaminants, such as particulates or debris entrainedin the gas flow, also present issues for reciprocating compressors.Ingress of liquid or other contaminants can damage a reciprocatingcompressor, and in particular may cause valve distress and failure.Ingress of such contaminants may also cause fouling of equipment,process drifting, and/or decreasing capacity and efficiency.

A turbine is a device that extracts energy from a fluid flow andconverts it into work, e.g., mechanical or electrical power. FIG. 6illustrates an example of a steam turbine 340 that may be used in aplant as described herein. The turbine 340 includes an inlet 341 thatreceives steam 342 from a steam source, a rotor 343 with blades 344 thatare acted on by the impulse of the steam 342 flowing through the inlet341 to turn the rotor 343, and a shaft 345 that is rotated by the rotor343 to generate mechanical power (e.g., by connection to gears) orelectrical power (e.g., by use of induction equipment). The steam thenescapes through an outlet 346. Heat generated by various components ofthe plant may be harnessed to create steam at the steam source, which ispassed to the steam turbine 340 for conversion to mechanical orelectrical power.

Issues faced by turbines in petrochemical plants include failure of tripand throttle valves, damage to flow path components (e.g., stationary orrotating blades) due to “wet” steam that is not of sufficiently hightemperature, and failure of the turbine for mechanical reasons.

In various embodiments described herein, as described in further detailbelow, different types of sensors may be used in and around rotatingequipment components such as compressors and turbines, includingcentrifugal compressors, axial compressors, reciprocating compressors,and/or steam turbines as described above. Data from such sensors canthen be analyzed in a manual and/or automated manner, and correctiveactions or recommendations for such actions can be generated based onsuch analysis. It is understood that any sensor described herein may beconfigured for communicating the data gathered by the sensor to acomputer system, including by various wired or wireless technologies. Inone or more embodiments, each sensor described herein may include awireless transmitter (or transceiver) for wirelessly communicating witha computer system. In another embodiment, some or all of the sensorsdescribed herein may include an individual processor and/or memoryconfigured for processing communications to/from the computer system orprocessing and/or storing data independently or in conjunction with thecomputer system.

Sensor Data Collection and Processing

The system may include one or more computing devices or platforms forcollecting, storing, processing, and analyzing data from one or moresensors. FIG. 11A depicts an illustrative computing system that may beimplemented at one or more components, pieces of equipment (e.g.,rotating equipment), and/or plants. FIG. 11A-FIG. 11E (hereinaftercollectively “FIG. 11”), show, by way of illustration, variouscomponents of the illustrative computing system in which aspects of thedisclosure may be practiced. It is to be understood that othercomponents may be used, and structural and functional modifications maybe made, in one or more other embodiments without departing from thescope of the present disclosure. Moreover, various connections betweenelements are discussed in the following description, and theseconnections are general and, unless specified otherwise, may be director indirect, wired or wireless, and/or combination thereof, and that thespecification is not intended to be limiting in this respect.

FIG. 11A depicts an illustrative operating environment in which variousaspects of the present disclosure may be implemented in accordance withexample embodiments. The computing system environment 1000 illustratedin FIG. 11A is only one example of a suitable computing environment andis not intended to suggest any limitation as to the scope of use orfunctionality contained in the disclosure. The computing systemenvironment 1000 may include various sensor, measurement, and datacapture systems, a data collection platform 1002, a data analysisplatform 1004, a control platform 1006, one or more networks, one ormore remote devices 1054, 1056, and/or one or more other elements. Thenumerous elements of the computing system environment of FIG. 11A may becommunicatively coupled through one or more networks. For example, thenumerous platforms, devices, sensors, and/or components of the computingsystem environment may be communicatively coupled through a privatenetwork 1008. The sensors be positioned on various components in theplant and may communicate wirelessly or wired with one or more platformsillustrated in FIG. 11A. The private network 1008 may include, in someexamples, a network firewall device to prevent unauthorized access tothe data and devices on the private network 1008. Alternatively oradditionally, the private network 1008 may be isolated from externalaccess through physical means, such as a hard-wired network with noexternal, direct access point. The data communicated on the privatenetwork 1008 may be optionally encrypted for further security. Dependingon the frequency of collection and transmission of sensor measurementsand other data to the data collection platform 1002, the private network1008 may experience large bandwidth usage and be technologicallydesigned and arranged to accommodate for such technological issues.Moreover, the computing system environment 1000 may also include apublic network 1010 that may be accessible to remote devices (e.g.,remote device 1054, remote device 1056). In some examples, a remotedevice may be located not in the proximity (e.g., more than one mileaway) of the various sensor, measurement, and data capture systemsillustrated in FIG. 11A. In other examples, the remote device may bephysically located inside a plant, but restricted from access to theprivate network 1008; in other words, the adjective “remote,” need notnecessarily require the device to be located at a great distance fromthe sensor systems and other components.

Although the computing system environment of FIG. 11A illustrateslogical block diagrams of numerous platforms and devices, the disclosureis not so limited. In particular, one or more of the logical boxes inFIG. 11 may be combined into a single logical box or the functionalityperformed by a single logical box may be divided across multipleexisting or new logical boxes. For example, aspects of the functionalityperformed by the data collection platform 1002 may be incorporated intoone or each of the sensor devices illustrated in FIG. 11A. As such, thedata collection may occur local to the sensor device, and the enhancedsensor system may communicate directly with one or more of the controlplatform 1006 and/or data analysis platform 1004. An illustrativeexample of such an embodiment is contemplated by FIG. 11A. Moreover, insuch an embodiment, the enhanced sensor system may measure values commonto a sensor, but may also filter the measurements such just those valuesthat are statistically relevant or of-interest to the computing systemenvironment are transmitted by the enhanced sensor system. As a result,the enhanced sensor system may include a processor (or other circuitrythat enables execution of computer instructions) and a memory to storethose instructions and/or filtered data values. The processor may beembodied as an application-specific integrated circuit (ASIC), FPGA, orother hardware- or software-based module for execution of instructions.In another example, one or more sensors illustrated in FIG. 11A may becombined into an enhanced, multi-purpose sensor system. Such a combinedsensor system may provide economies of scale with respect to hardwarecomponents such as processors, memories, communication interfaces, andothers.

In yet another example, the data collection platform 1002 and dataanalysis platform 1004 may reside on a single server computer anddepicted as a single, combined logical box on a system diagram.Moreover, a data store may be illustrated in FIG. 11A separate and apartfrom the data collection platform 1002 and data analysis platform 1004to store a large amount of values collected from sensors and othercomponents. The data store may be embodied in a database format and maybe made accessible to the public network 1010; meanwhile, the controlplatform 1006, data collection platform 1002, and data analysis platform1004 may be restricted to the private network 1008 and left inaccessibleto the public network 1010. As such, the data collected from a plant maybe shared with users (e.g., engineers, data scientists, others), acompany's employees, and even third parties (e.g., subscribers to thecompany's data feed) without compromising potential securityrequirements related to operation of a plant. The data store may beaccessible to one or more users and/or remote devices over the publicnetwork 1010.

Referring to FIG. 11A, process measurements from various sensor andmonitoring devices may be used to monitor conditions in, around, and onprocess equipment (e.g., rotating equipment). Such sensors may include,but are not limited to, pressure sensors 1024, differential pressuresensors 1036, various flow sensors (including but not limited to orificeplate type 1013, disc sensors 1022, venturi 1038, other flow sensors1030), temperature sensors 1012 including thermal cameras 1020 and skinthermocouples, capacitance sensors 1034, weight sensors 1032, gaschromatographs 1014, moisture sensors 1016, ultrasonic sensors 1018,position sensors, timing sensors, vibration sensors 1026, microphones1028, level sensors 1046, liquid level (hydraulic fluid) sensors, andother sensors used in the refining and petrochemical industry. Further,process laboratory measurements may be taken using gas chromatographs1014, liquid chromatographs, distillation measurements, octanemeasurements, and other laboratory measurements. System operationalmeasurements also can be taken to correlate the system operation to therotating equipment measurements.

In addition, sensors may include transmitters and/or deviation alarms.One or more sensors may be programmed to set off an alarm or alert. Forexample, if an actuator fails, sensor data may be used to automaticallytrigger an alarm or alert (e.g., an audible alarm or alert, a visualalarm or alert). Other sensors may transmit signals to a processor or ahub that collects the data and sends to a processor. For example,temperature and pressure measurements may be sent to a hub (e.g., datacollection platform 1002). In one or more embodiments, temperaturesensors 1012 may include thermocouples, fiber optic temperaturemeasurement, thermal cameras 1020, and/or infrared cameras. Skinthermocouples may be applied to rotating equipment casing, oralternatively, to tubes, plates, or placed directly on a wall of arotating equipment component. Alternatively, thermal (infrared) cameras1020 may be used to detect temperature in one or more aspects of theequipment. A shielded (insulated) tube skin thermocouple assembly may beused to obtain accurate measurements. One example of a thermocouple maybe a removable Xtracto™ Pad. A thermocouple can be replaced without anyadditional welding. Clips and/or pads may be used for ease ofreplacement. Fiber Optic cable can be attached to the pipe, line, and/orvessel to provide a complete profile of temperatures.

Sensors may be also used throughout a plant or rotating equipment todetect and monitor various issues such as PV detection, surge detection,fouling, gas quality, dew point characteristics, and/or productionlevels. Sensors might be able to detect whether feed composition intothe rotating equipment, such as pH, are outside of acceptable rangesleading to a corrosive environment or whether consumption of sacrificialanodes (in water services) is nearing completion and resulting in acorrosive environment. Sensors detecting outlet temperatures andpressure drops may be used to determine/predict flow and production ratechanges.

Furthermore, flow sensors may be used in flow paths such as the inlet tothe path, outlet from the path, or within the path. If multiple tubesare used, the flow sensors may be placed in corresponding positions ineach of the rotating machines. In this manner, one can determine if oneof the rotating machines is behaving abnormally compared to one or moreothers. Flow may be determined by pressure-drop across a knownresistance, such as by using pressure taps. In other examples, flow maybe inferred using fluid density in addition to suction and dischargepressures. Other types of flow sensors include, but are not limited to,ultrasonic, turbine meter, hot wire anemometer, vane meter, Kármán™,vortex sensor, membrane sensor (membrane has a thin film temperaturesensor printed on the upstream side, and one on the downstream side),tracer, radiographic imaging (e.g. identify two-phase vs. single-phaseregion of channels), an orifice plate (e.g., which may in some examples,be placed in front of one or more tube or channels), pitot tube, thermalconductivity flow meter, anemometer, internal pressure flow profile.

Sensor data, process measurements, and/or calculations made using thesensor data or process measurements may be used to monitor and/orimprove the performance of the equipment and parts making up theequipment, as discussed in further detail below. For example, sensordata may be used to detect that a desirable or an undesirable chemicalreaction is taking place within a particular piece of equipment, and oneor more actions may be taken to encourage or inhibit the chemicalreaction. Chemical sensors may be used to detect the presence of one ormore chemicals or components in the streams, such as corrosive species,oxygen, hydrogen, and/or water (moisture). Chemical sensors may use gaschromatographs, liquid chromatographs, distillation measurements, and/oroctane measurements. In another example, equipment information, such aswear, efficiency, production, state, or other condition information, maybe gathered and determined based on sensor data. Corrective action maybe taken based on determining this equipment information. For example,if the equipment is showing signs of wear or failure, corrective actionsmay be taken, such as taking an inventory of parts to ensure replacementparts are available, ordering replacement parts, and/or calling inrepair personnel to the site. Certain parts of equipment may be replacedimmediately. Other parts may be safe to use, but a monitoring schedulemay be adjusted. Alternatively or additionally, one or more inputs orcontrols relating to a process may be adjusted as part of the correctiveaction. These and other details about the equipment, sensors, processingof sensor data, and actions taken based on sensor data are described infurther detail below.

Monitoring the rotating equipment and the processes using rotatingequipment includes collecting data that can be correlated and used topredict behavior or problems in different rotating equipment used in thesame plant or in other plants and/or processes. Data collected from thevarious sensors (e.g., measurements such as flow, pressure drop, thermalperformance, vessel skin temperature at the top, expansion bellows leak,vibration, etc.) may be correlated with external data, such asenvironmental or weather data. Process changes or operating conditionsmay be able to be altered to preserve the equipment until the nextscheduled maintenance period. Fluids may be monitored for corrosivecontaminants and pH may monitored in order to predict higher than normalcorrosion rates within the rotating equipment.

Systems Facilitating Sensor Data Collection

Sensor data may be collected by a data collection platform 1002. Thesensors may interface with the data collection platform 1002 via wiredor wireless transmissions. The data collection platform 1002 maycontinuously or periodically (e.g., every second, every minute, everyhour, every day, once a week, once a month) transmit collected sensordata to a data analysis platform 1004, which may be nearby or remotefrom the data collection platform 1002.

Sensor data (e.g., temperature data) may be collected continuously or atperiodic intervals (e.g., every second, every five seconds, every tenseconds, every minute, every five minutes, every ten minutes, everyhour, every two hours, every five hours, every twelve hours, every day,every other day, every week, every other week, every month, every othermonth, every six months, every year, or another interval). Data may becollected at different locations at different intervals. For example,data at a known hot spot may be collected at a first interval, and dataat a spot that is not a known hot spot may be collected at a secondinterval. The data collection platform transmit collected sensor data toa data analysis platform, which may be nearby or remote from the datacollection platform.

The computing system environment of FIG. 11A includes logical blockdiagrams of numerous platforms and devices that are further elaboratedupon in FIG. 11B, FIG. 11C, FIG. 11D, and FIG. 11E. FIG. 11B is anillustrative data collection platform 1002. FIG. 11C is an illustrativedata analysis platform 1004. FIG. 11D is an illustrative controlplatform 1006. FIG. 11E is an illustrative remote device 1054. Theseplatforms and devices of FIG. 11 include one or more processing units(e.g., processors) to implement the methods and functions of certainaspects of the present disclosure in accordance with the exampleembodiments. The processors may include general-purpose microprocessorsand/or special-purpose processors designed for particular computingsystem environments or configurations. For example, the processors mayexecute computer-executable instructions in the form of software and/orfirmware stored in the memory of the platform or device. Examples ofcomputing systems, environments, and/or configurations that may besuitable for use with the disclosed embodiments include, but are notlimited to, personal computers (PCs), server computers, hand-held orlaptop devices, smart phones, multiprocessor systems,microprocessor-based systems, programmable consumer electronics, networkPCs, minicomputers, mainframe computers, distributed computingenvironments that include any of the above systems or devices, and thelike.

In addition, the platform and/or devices in FIG. 11 may include one ormore memories include any of a variety of computer readable media.Computer-readable media may be any available media that may be accessedby the data collection platform 1002, may be non-transitory, and mayinclude volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, object code, data structures,database records, program modules, or other data. Examples ofcomputer-readable media may include random access memory (RAM), readonly memory (ROM), electronically erasable programmable read only memory(EEPROM), flash memory or other memory technology, compact diskread-only memory (CD-ROM), digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to store the desired information and that can be accessed by thedata collection platform 1002. The memories in the platform and/ordevices may further store modules that may include compiled softwarecode that causes the platform, device, and/or overall system to operatein a technologically improved manner as disclosed herein. For example,the memories may store software used by a computing platform, such asoperating system, application programs, and/or associated database.

Furthermore, the platform and/or devices in FIG. 11 may include one ormore communication interfaces including, but are not limited to, amicrophone, keypad, keyboard, touch screen, and/or stylus through whicha user of a computer (e.g., a remote device) may provide input, and mayalso include a speaker for providing audio output and a video displaydevice for providing textual, audiovisual and/or graphical output. Inputmay be received via one or more graphical user interfaces, which may bepart of one or more dashboards (e.g., dashboard 1003, dashboard 1005,dashboard 1007). The communication interfaces may include a networkcontroller for electronically communicating (e.g., wirelessly or wired)over a public network 1010 or private network 1008 with one or moreother components on the network. The network controller may includeelectronic hardware for communicating over network protocols, includingTCP/IP, UDP, Ethernet, and other protocols.

In some examples, one or more sensor devices in FIG. 11A may be enhancedby incorporating functionality that may otherwise be found in a datacollection platform 1002. These enhanced sensor system may providefurther filtering of the measurements and readings collected from theirsensor devices. For example, with some of the enhanced sensor systems inthe operating environment illustrated in FIG. 11A, an increased amountof processing may occur at the sensor so as to reduce the amount of dataneeding to be transferred over a private network 1008 in real-time to acomputing platform. The enhanced sensor system may filter at the sensoritself the measured/collected/captured data and only particular,filtered data may be transmitted to the data collection platform 1002for storage and/or analysis.

Referring to FIG. 11B, in one or more embodiments, a data collectionplatform 1002 may include one or more processors 1060, one or morememories 1062, and communication interfaces 1068. The memory 1062 mayinclude a database 1064 for storing data records of various valuescollected from one or more sources. In addition, a data collectionmodule 1066 may be stored in the memory 1062 and assist the processor1060 in the data collection platform 1002 in communicating with, via thecommunications interface 1068, one or more sensor, measurement, and datacapture systems, and processing the data received from these sources. Insome embodiments, the data collection module 1066 may includecomputer-executable instructions that, when executed by the processor1060, cause the data collection platform 1002 to perform one or more ofthe steps disclosed herein. In other embodiments, the data collectionmodule 1066 may be a hybrid of software-based and/or hardware-basedinstructions to perform one or more of the steps disclosed herein. Insome examples, the data collection module 1066 may assist an enhancedsensor system with further filtering the measurements and readingscollected from the sensor devices. Although the elements of FIG. 11B areillustrated as logical block diagrams, the disclosure is not so limited.In particular, one or more of the logical boxes in FIG. 11B may becombined into a single logical box or the functionality performed by asingle logical box may be divided across multiple existing or newlogical boxes. Moreover, some logical boxes that are visually presentedas being inside of another logical box may be moved such that they arepartially or completely residing outside of that logical box. Forexample, while the database 1064 in FIG. 11B is illustrated as beingstored inside one or more memories 1062 in the data collection platform1002, FIG. 11B contemplates that the database 1064 may be stored in astandalone data store communicatively coupled to the data collectionmodule 1066 and processor 1060 of the data collection platform 1002 viathe communications interface 1068 of the data collection platform 1002.

In addition, the data collection module 1066 may assist the processor1060 in the data collection platform 1002 in communicating with, via thecommunications interface 1068, and processing data received from othersources, such as data feeds from third-party servers and manual entry atthe field site from a dashboard graphical user interface (e.g., viadashboard 1003). For example, a third-party server may providecontemporaneous weather data to the data collection module. Someelements of chemical and petrochemical/refinery plants may be exposed tothe outside and thus may be exposed to various environmental stresses.Such stresses may be weather related such as temperature extremes (hotand cold), high wind conditions, and precipitation conditions such assnow, ice, and rain. Other environmental conditions may be pollutionparticulates such as dust and pollen, or salt if located near an ocean,for example. Such stresses can affect the performance and lifetime ofequipment in the plants. Different locations may have differentenvironmental stresses. For example, a refinery in Texas will havedifferent stresses than a chemical plant in Montana. In another example,data manually entered from a dashboard graphical user interface (e.g.,via dashboard 1003) (or other means) may be collected and saved intomemory by the data collection module. Production rates may be enteredand saved in memory. Tracking production rates may indicate issues withflows. For example, as fouling occurs, the production rate may fall if aspecific outlet temperature can no longer be achieved at the targetedcapacity and capacity has to be reduced to maintain the targeted outlettemperature.

Referring to FIG. 11C, in one or more embodiments, a data analysisplatform 1004 may include one or more processors 1070, one or morememories 1072, and communication interfaces 1082. The memory 1072 mayinclude a database 1074 for storing data records of various valuescollected from one or more sources. Alternatively or additionally, thedatabase 1074 may be the same database as that depicted in FIG. 11B andthe data analysis platform 1004 may communicatively couple with thedatabase 1074 via the communication interface of the data analysisplatform 1004. At least one advantage of sharing a database between thetwo platforms is the reduced memory requirements due to not duplicatingthe same or similar data. In addition, a data analysis module 1076 maybe stored in the memory 1072 and assist the processor 1070 in the dataanalysis platform 1004 in processing and analyzing the data valuesstored in the database 1074. In some embodiments, the data analysismodule 1076 may include computer-executable instructions that, whenexecuted by the processor 1070, cause the data analysis platform 1004 toperform one or more of the steps disclosed herein. In other embodiments,the data analysis module 1076 may be a hybrid of software-based and/orhardware-based instructions to perform one or more of the stepsdisclosed herein. In some embodiments, the data analysis module 1076 mayperform statistical analysis, predictive analytics, and/or machinelearning on the data values in the database 1074 to generate predictionsand models. For example, the data analysis platform 1004 may analyzesensor data to detect new problems and/or to monitor existing problems(e.g., to determine if an existing problem is growing, maintaining thesame severity, or shrinking) in the equipment of a plant. The dataanalysis platform 1004 may compare temperature or other data fromdifferent dates to determine if changes are occurring. Such comparisonsmay be made on a monthly, weekly, daily, hourly, real-time, or someother basis.

Referring to FIG. 11C, the recommendation module 1078 in the dataanalysis platform 1004 may coordinate with the data analysis module 1076to generate recommendations for adjusting one or more parameters for theoperation of the plant environment depicted in FIG. 11A. In someembodiments, the recommendation module 1078 may communicate therecommendation to the command module 1080, which may generate commandcodes that may be transmitted, via the communications interface, tocause adjustments or halting/starting of one or more operations in theplant environment. The command codes may be transmitted to a controlplatform 1006 for processing and/or execution. In one or moreembodiments, the command codes may be directly communicated, eitherwirelessly or in a wired fashion, to physical components at the plantsuch that the physical components include an interface to receive thecommands and execute on them.

Although the elements of FIG. 11C are illustrated as logical blockdiagrams, the disclosure is not so limited. In particular, one or moreof the logical boxes in FIG. 11C may be combined into a single logicalbox or the functionality performed by a single logical box may bedivided across multiple existing or new logical boxes. Moreover, somelogical boxes that are visually presented as being inside of anotherlogical box may be moved such that they are partially or completelyresiding outside of that logical box. For example, while the database isvisually depicted in FIG. 11C as being stored inside one or morememories in the data analysis platform 1004, FIG. 11C contemplates thatthe database may be stored in a standalone data store communicativelycoupled to the data analysis module and processor of the data analysisplatform 1004 via the communications interface of the data analysisplatform 1004. Furthermore, the databases from multiple plant locationsmay be shared and holistically analyzed to identify one or more trendsand/or patterns in the operation and behavior of the plant and/or plantequipment. In such a crowdsourcing-type example, a distributed databasearrangement may be provided where a logical database may simply serve asan interface through which multiple, separate databases may be accessed.As such, a computer with predictive analytic capabilities may access thelogical database to analyze, recommend, and/or predict the behavior ofone or more aspects of plants and/or equipment. In another example, thedata values from a database from each plant may be combined and/orcollated into a single database where predictive analytic engines mayperform calculations and prediction models.

Referring to FIG. 11D, in one or more embodiments, a control platform1006 may include one or more processors 1084, one or more memories 1086,and communication interfaces 1092. The memory 1086 may include adatabase 1088 for storing data records of various values transmittedfrom a user interface, computing device, or other platform. The valuesmay include parameter values for particular equipment at the plant. Forexample, some illustrative equipment at the plant that may be configuredand/or controlled by the control platform 1006 include, but is notlimited to, a feed switcher 1042, sprayer 1052, one or more valves 1044,one or more pumps 1040, one or more gates 1048, and/or one or moredrains 1050. In addition, a control module 1090 may be stored in thememory and assist the processor in the control platform 1006 inreceiving, storing, and transmitting the data values stored in thedatabase. In some embodiments, the control module 1090 may includecomputer-executable instructions that, when executed by the processor1084, cause the control platform 1006 to perform one or more of thesteps disclosed herein. In other embodiments, the control module may bea hybrid of software-based and/or hardware-based instructions to performone or more of the steps disclosed herein.

In a plant environment such as illustrated in FIG. 11A, if sensor datais outside of a safe range, this may be cause for immediate danger. Assuch, there may be a real-time component to the system such that thesystem processes and responds in a timely manner. Although in someembodiments, data could be collected and leisurely analyzed over alengthy period of months, numerous embodiments contemplate a real-timeor near real-time responsiveness in analyzing and generating alerts,such as those generated or received by the alert module in FIG. 11E.

Referring to FIG. 11E, in one or more embodiments, a remote device 1054may include one or more processors 1093, one or more memories 1094, andcommunication interfaces 1099. The memory 1094 may include a database1095 for storing data records of various values entered by a user orreceived through the communications interface. In addition, an alertmodule 1096, command module 1097, and/or dashboard module 1098 may bestored in the memory 1094 and assist the processor 1093 in the remotedevice 1054 in processing and analyzing the data values stored in thedatabase. In some embodiments, the aforementioned modules may includecomputer-executable instructions that, when executed by the processor,cause the remote device 1054 to perform one or more of the stepsdisclosed herein. In other embodiments, the aforementioned modules maybe a hybrid of software-based and/or hardware-based instructions toperform one or more of the steps disclosed herein. In some embodiments,the aforementioned modules may generate alerts based on values receivedthrough the communications interface. The values may indicate adangerous condition or even merely a warning condition due to odd sensorreadings. The command module 1097 in the remote device 1054 may generatea command that when transmitted through the communications interface tothe platforms at the plant, causes adjusting of one or more parameteroperations of the plant environment depicted in FIG. 11A. In someembodiments, the dashboard module 1098 may display a graphical userinterface to a user of the remote device 1054 to enable the user toenter desired parameters and/or commands. These parameters/commands maybe transmitted to the command module 1097 to generate the appropriateresulting command codes that may be then transmitted, via thecommunications interface, to cause adjustments or halting/starting ofone or more operations in the plant environment. The command codes maybe transmitted to a control platform 1006 for processing and/orexecution. In one or more embodiments, the command codes may be directlycommunicated, either wirelessly or in a wired fashion, to physicalcomponents at the plant such that the physical components include aninterface to receive the commands and execute them.

Although FIG. 11E is not so limited, in some embodiments the remotedevice 1054 may include a desktop computer, a smartphone, a wirelessdevice, a tablet computer, a laptop computer, and/or the like. Theremote device 1054 may be physically located locally or remotely, andmay be connected by one of communications links to the public network1010 that is linked via a communications link to the private network1008. The network used to connect the remote device 1054 may be anysuitable computer network including the Internet, an intranet, awide-area network (WAN), a local-area network (LAN), a wireless network,a digital subscriber line (DSL) network, a frame relay network, anasynchronous transfer mode (ATM) network, a virtual private network 1008(VPN), or any combination of any of the same. Communications links maybe any communications links suitable for communicating betweenworkstations and server, such as network links, dial-up links, wirelesslinks, hard-wired links, as well as network types developed in thefuture, and the like. Various protocols such as transmission controlprotocol/Internet protocol (TCP/IP), Ethernet, file transfer protocol(FTP), hypertext transfer protocol (HTTP) and the like may be used, andthe system can be operated in a client-server configuration to permit auser to retrieve web pages from a web-based server. Any of variousconventional web browsers can be used to display and manipulate data onweb pages.

Although the elements of FIG. 11E are illustrated as logical blockdiagrams, the disclosure is not so limited. In particular, one or moreof the logical boxes in FIG. 11E may be combined into a single logicalbox or the functionality performed by a single logical box may bedivided across multiple existing or new logical boxes. Moreover, somelogical boxes that are visually presented as being inside of anotherlogical box may be moved such that they are partially or completelyresiding outside of that logical box. For example, while the database isvisually depicted in FIG. 11E as being stored inside one or morememories in the remote device 1054, FIG. 11E contemplates that thedatabase may be stored in a standalone data store communicativelycoupled, via the communications interface, to the modules stored at theremote device 1054 and processor of the remote device 1054.

Referring to FIG. 11, in some examples, the performance of operation ina plant may be improved by using a cloud computing infrastructure andassociated methods, as described in US Patent Application PublicationNo. US2016/0260041, which was published Sep. 8, 2016, and which isherein incorporated by reference in its entirety. The methods mayinclude, in some examples, obtaining plant operation information fromthe plant and/or generating a plant process model using the plantoperation information. The method may include receiving plant operationinformation over the Internet, or other computer network (includingthose described herein) and automatically generating a plant processmodel using the plant operation information. These plant process modelsmay be configured and used to monitor, predict, and/or optimizeperformance of individual process units, operating blocks and/orcomplete processing systems. Routine and frequent analysis of predictedversus actual performance may further allow early identification ofoperational discrepancies, which may be acted upon to optimize impact.

The aforementioned cloud computing infrastructure may use a datacollection platform 1002 associated with a plant to capture data, e.g.,sensor measurements, which may be automatically sent to the cloudinfrastructure, which may be remotely located, where it may be reviewedto, for example, eliminate errors and biases, and used to calculate andreport performance results. The data collection platform 1002 mayinclude an optimization unit that acquires data from a customer site,other site, and/or plant (e.g., sensors and other data collectors at aplant) on a recurring basis. For cleansing, the data may be analyzed forcompleteness and corrected for gross errors by the optimization unit.The data may also be corrected for measurement issues (e.g., an accuracyproblem for establishing a simulation steady state) and overall massbalance closure to generate a duplicate set of reconciled plant data.The corrected data may be used as an input to a simulation process, inwhich the process model is tuned to ensure that the simulation processmatches the reconciled plant data. An output of the reconciled plantdata may be used to generate predicted data using a collection ofvirtual process model objects as a unit of process design.

The performance of the plant and/or individual process units of theplant is/are compared to the performance predicted by one or moreprocess models to identify any operating differences or gaps.Furthermore, the process models and collected data (e.g., plantoperation information) may be used to run optimization routines thatconverge on an optimal plant operation for a given values of, e.g.,feed, products, and/or prices. A routine may be understood to refer to asequence of computer programs or instructions for performing aparticular task.

The data analysis platform 1004 may include an analysis unit thatdetermines operating status, based on at least one of a kinetic model, aparametric model, an analytical tool, and/or a related knowledge and/orbest practice standard. The analysis unit may receive historical and/orcurrent performance data from one or a plurality of plants toproactively predict one or more future actions to be performed. Topredict various limits of a particular process and stay within theacceptable range of limits, the analysis unit may determine targetoperational parameters of a final product based on actual current and/orhistorical operational parameters. This evaluation by the analysis unitmay be used to proactively predict future actions to be performed. Inanother example, the analysis unit may establish a boundary or thresholdof an operating parameter of the plant based on at least one of anexisting limit and an operation condition. In yet another example, theanalysis unit may establish a relationship between at least twooperational parameters related to a specific process for the operationof the plant. Finally in yet another example, one or more of theaforementioned examples may be performed with or without a combinationof the other examples.

The plant process model predicts plant performance that is expectedbased upon the plant operation information. The plant process modelresults can be used to monitor the health of the plant and to determinewhether any upset or poor measurement occurred. The plant process modelis desirably generated by an iterative process that models at variousplant constraints to determine the desired plant process model.

Using a web-based system for implementing the method of this disclosuremay provide one or more benefits, such as improved plant performance dueto an increased ability by plant operators to identify and captureopportunities, a sustained ability to bridge plant performance gaps,and/or an increased ability to leverage personnel expertise and improvetraining and development. Some of the methods disclosed herein allow forautomated daily evaluation of process performance, thereby increasingthe frequency of performance review with less time and effort requiredfrom plant operations staff.

Further, the analytics unit may be partially or fully automated. In oneor more embodiments, the system is performed by a computer system, suchas a third-party computer system, remote from the plant and/or the plantplanning center. The system may receive signals and parameters via thecommunication network, and displays in real time related performanceinformation on an interactive display device accessible to an operatoror user. The web-based platform allows all users to work with the sameinformation, thereby creating a collaborative environment for sharingbest practices or for troubleshooting. The method further provides moreaccurate prediction and optimization results due to fully configuredmodels. Routine automated evaluation of plant planning and operationmodels allows timely plant model tuning to reduce or eliminate gapsbetween plant models and the actual plant performance. Implementing theaforementioned methods using the web-based platform also allows formonitoring and updating multiple sites, thereby better enabling facilityplanners to propose realistic optimal targets.

FIGS. 12A-12B depict illustrative system flow diagrams in accordancewith one or more embodiments described herein. As shown in FIG. 12A, instep 201, data collection platform 1002 may collect sensor data. In step202, data collection platform 1002 may transmit sensor data to dataanalysis platform 1004. In step 203, data analysis platform 1004 mayanalyze data. In step 204, data analysis platform 1004 may send an alertto remote device 1054 and/or remote device 1056.

As shown in FIG. 12B, in step 205, data analysis platform 1004 mayreceive a command from remote device 1054 and/or remote device 1056. Insome embodiments, the control platform 1006 may receive the command fromremote device 1054 and/or remote device 1056. In step 206, data analysisplatform 1004 may send a command to control platform 1006. In someembodiments, the command may be similar to the command received fromremote device 1054 and/or remote device 1056. In some embodiments, dataanalysis platform 1004 may perform additional analysis based on thereceived command from remote device 1054 and/or remote device 1056before sending a command to control platform 1006. In step 207, controlplatform 1006 may take corrective action. The corrective action may bebased on the command received from data analysis platform 1004, remotedevice 1054, and/or remote device 1056. The corrective action may berelated to one or more pieces of equipment (e.g., rotating equipment)associated with sensors that collected the sensor data in step 201.

FIG. 15 depicts an illustrative flow diagram in accordance with one ormore embodiments described herein. The flow may be performed by one ormore devices, which may be interconnected via one or more networks.

First, the one or more devices may collect 1402 sensor data. The sensordata may be from one or more sensors attached to one or more pieces ofequipment (e.g., rotating equipment) in a plant. The sensor data may belocally collected and processed and/or may be locally collected andtransmitted for processing.

After the sensor data is collected, the one or more devices may process1404 the sensor data. The one or more devices may compare the data topast data from the one or more pieces of equipment, other pieces ofequipment at a same plant, one or more pieces of equipment at adifferent plant, manufacturer recommendations or specifications, or thelike.

After the sensor data is processed, the one or more devices maydetermine 1406 one or more recommendations based on the sensor data. Theone or more recommendations may include recommendations of one or moreactions to take based on the sensor data.

The one or more devices may send 1408 one or more alerts, which mayinclude the determined recommendation. The one or more alerts mayinclude information about the sensor data, about other data, or thelike.

The one or more devices may receive 1410 a command to take an action(e.g., the recommended action, an action other than the recommendedaction, or no action). After receiving the command, the one or moredevices may take 1412 the action. The action may, in some embodiments,include one or more corrective actions, which may cause one or morechanges in the operation of the one or more pieces of equipment. Thecorrective action(s) may be taken automatically or after userconfirmation, and/or the corrective action(s) may be taken without anaccompanying alert being generated (and vice-versa).

FIG. 13 depicts an illustrative graphical user interface 1200 of anapplication that may be used for providing information received from oneor more sensors or determined based on analyzing information receivedfrom one or more sensors, according to one or more embodiments describedherein. The graphical user interface may be displayed as part of asmartphone application (e.g., running on a remote device, such as remotedevice 1054 or remote device 1056), a desktop application, a webapplication (e.g., that runs in a web browser), a web site, anapplication running on a plant computer, or the like.

The graphical user interface 1200 may include one or more visualrepresentations of data (e.g., chart, graph, etc.) that showsinformation about a plant, a particular piece of equipment in a plant,or a process performed by a plant or a particular piece or combinationof equipment in the plant. For example, a graph may show informationabout an operating condition, an efficiency, a production level, or thelike. The graphical user interface 1200 may include a description of theequipment, the combination of equipment, or the plant to which thevisual display of information pertains.

The graphical user interface 1200 may display the information for aparticular time or period of time (e.g., the last five minutes, the lastten minutes, the last hour, the last two hours, the last 12 hours, thelast 24 hours, etc.). The graphical user interface may be adjustable toshow different ranges of time, automatically or based on user input.

The graphical user interface 1200 may include one or more buttons thatallow a user to take one or more actions. For example, the graphicaluser interface may include a button (e.g., an “Actions” button) that,when pressed, shows one or more actions available to the user. Thegraphical user interface may include a button (e.g., a “Change View”button) that, when pressed, changes one or more views of one or moreelements of the graphical user interface. The graphical user interfacemay include a button (e.g., a “Settings” button) that, when pressed,shows one or more settings of the application of which the graphicaluser interface is a part. The graphical user interface may include abutton (e.g., a “Refresh Data” button) that, when pressed, refreshesdata displayed by the graphical user interface. In some aspects, datadisplayed by the graphical user interface may be refreshed in real time,according to a preset schedule (e.g., every five seconds, every tenseconds, every minute, etc.), and/or in response to a refresh requestreceived from a user. The graphical user interface may include a button(e.g., a “Send Data” button) that, when pressed, allows a user to senddata to one or more other devices. For example, the user may be able tosend data via email, SMS, text message, iMessage, FTP, cloud sharing,AirDrop, or via some other method. The user may be able to select one ormore pieces of data, graphics, charts, graphs, elements of the display,or the like to share or send. The graphical user interface may include abutton (e.g., an “Analyze Data” button) that, when pressed, causes oneor more data analysis functions to be performed. In some aspects, theuser may provide additional input about the desired data analysis, suchas desired input, desired output, desired granularity, desired time tocomplete the data analysis, desired time of input data, or the like.

FIG. 14 depicts an illustrative graphical user interface 1300 of anapplication that may be used for providing alerts and/or receiving orgenerating commands for taking corrective action, in accordance with oneor more embodiments described herein. The graphical user interface 1300may include an alert with information about a current state of a pieceof equipment (e.g., rotating equipment), a problem being experienced bya piece of equipment (e.g., rotating equipment), a problem with a plant,or the like.

The graphical user interface 1300 may include one or more buttons that,when pressed, cause one or more actions to be taken. For example, thegraphical user interface 1300 may include a button that, when pressed,causes a flow rate to change. In another example, the graphical userinterface 1300 may include a button that, when pressed, sends an alertto a contact (e.g., via a remote device), the alert includinginformation similar to the information included in the alert providedvia the graphical user interface. In a further example, the graphicaluser interface 1300 may include a button that, when pressed, shows oneor more other actions that may be taken (e.g., additional correctiveactions).

Reactor Loop Fouling Monitor

In one or more embodiments, one or more sensors may be used inconjunction with a centrifugal or axial compressor to detect potentialfouling in the equipment and allow corrective actions to be taken.Fouling can occur in multiple locations on a dynamic compressor, and afrequent source of problems is fouling from buildup of nitrogen salts onthe impeller blades, especially in a recycle gas compressor. If foulingcan be detected at an early stage, corrective actions can be taken toaddress the fouling rather than shutting down the entire process. Anautomated technique for detecting and managing potential fouling canprevent unscheduled shutdowns of equipment.

A centrifugal compressor 310 as shown in FIG. 2 or an axial compressor320 as shown in FIG. 3 may include one or more vibration sensors (A) 362mounted on the compressor shaft 318, 328 and configured to measurevibrations in the shaft. The vibration sensor(s) 355 may be a proximityprobe in one or more embodiments, and may be mounted adjacent to one ofthe bearings of the compressor 310, 320. In addition, a centrifugal oraxial compressor 310, 320 as shown in FIGS. 2-3 may include one or morepressure sensors 356 and, optionally, one or more temperature sensors(T) configured to measure the pressure (P) and temperature (T),respectively, within the compressor 310 and/or at suction/discharge. Inone or more embodiments, the compressor 310, 320 may include multiplepressure sensors 356 disposed after each of the impellers 313, 323, topermit measurement of the ΔP for each impeller 313, 323. Temperaturesensors 355 may likewise be disposed adjacent each impeller 313, 323 topermit measurement of temperature for each impeller 313, 323. FIG. 9Aillustrates this in connection with a centrifugal compressor 310, but itis understood that a similar configuration can be used with an axialcompressor, with sensors 355, 356 following each impeller 323. Suchsensors 355, 356 may be mounted in the stationary flow passage of thediffuser 316 following each impeller 313 of a centrifugal compressor inone or more embodiments. The sensors 355, 356 may be positioned at eachdiffuser 326 in an axial compressor 320. In another embodiment, thecompressor 310 may additionally or alternately include pressure sensors356 disposed within the inlet 311, 321 and outlet 317, 327, measuring Pbased on suction pressure and discharge pressure. Likewise, thetemperature sensors 355 may be disposed at each end of the shaft 318,328. One or both sensors 355, 356 may be disposed elsewhere in anotherembodiment. The sensors 355, 356, 362 are configured for continuous orsubstantially continuous detection, to permit collection of datadynamically during the operation of the compressor 310. This vibrationand pressure data may include one or more of: maximum/minimum P,maximum/minimum vibration, and a continuous track of P and/or vibrationthroughout the operation (e.g., multiple measurement points per second).Increased vibrations and decreased output pressures are often indicativeof fouling. Additional sensors may be positioned and configured formeasuring parameters such as temperature. For example, temperaturesensors could be located in similar positions as those described abovefor the pressure sensors 356. In other embodiments, the compressor 310may not include pressure, temperature, and vibration sensors. Forexample, in one or more embodiments, the compressor 310 may include onlypressure sensors 356 and temperature sensors 355, and in anotherembodiment, the compressor 310 may include only pressure sensors 356 andvibration sensors 362.

Sensors placed in and around the compressor can collect data relevant topotential fouling and transmit the data to a computer system, which cananalyze the data to determine whether fouling exists, determine a degreeof fouling, or predict a future occurrence and/or degree of fouling. Inparticular, the system may be configured to detect and/or predictfouling of the impeller blades for the compressor. In one or moreembodiments, pressure and vibration data collected by the sensors 355,356 may be used in this analysis. Additionally, the pressure andvibration data and analysis thereof may be based on the compressor as awhole or, in the case of a centrifugal compressor 310, may be based oneach individual impeller 313. Analysis of data on a per-impeller basiscan permit detection of whether fouling is limited to a particularimpeller or impellers, so that corrective actions can be taken withrespect to the particular impeller(s). The computer system may beconfigured to determine the extent of the vibration and pressure and/orthe deviation of the vibration and pressure from standard operation.

Sensor information may be gathered by one or more sensors andtransmitted to data collection platform. Data collection platform maytransmit the collected sensor data to data analysis platform, which maybe at a plant or remote from a plant (e.g., in the cloud). Data analysisplatform may analyze the received sensor data. Data analysis platformmay compare the sensor data to one or more rules to determine if any ofthe issues disclosed herein are occurring. For example, detecting offouling of the impeller blades for a compressor may be indicated if inone or more conditions are met: (1) sensing of increased vibrationsand/or (2) measuring of decreased output pressure. Furthermore, dataanalysis platform may compare current sensor data to past sensor datafrom the rotating equipment, from other rotating equipment at the sameplant, from other rotating equipment at other plants, from amanufacturer, or the like. Data analysis platform may determine if oneor more data characteristics of the sensor data match data that mayindicate any of the issues disclosed herein.

Data analysis platform may further run process simulations to suggestchanges to operating parameters of the rotating equipment and associatedcomponents to avoid or limit further damage by one or more of the issuesdisclosed herein. In some aspects, data analysis platform maycommunicate with one or more vendors regarding the results of thesimulation, and receive recommendations from the vendor on how to changeor optimize parameters (e.g., geometry) of the equipment. Data analysisplatform may use this information to create or expand a searchabledatabase.

In one or more embodiments, the P and vibration data may be compared tocurrent or archived P and vibration data for the same compressor and/orother compressors, and the computer system can analyze the data to makeuseful determinations, such as whether the data indicates that potentialfouling exists or will exist and/or making predictions regarding futureoperation. Corrective actions can be taken if deviations are determinedto exist, and if such deviations are determined to be potentiallyindicative of fouling. The data comparison may be made across a varietyof time frames, from a time frame of a few minutes or hours, toreal-time continuous comparison, to historical comparison over a periodof months or more, and may include absolute and proportionalcomparisons. As one example, fouling may be detected if the P and/orvibration of the compressor (or an individual impeller) is found todiffer by a set percentage (e.g., +/−5% or 10%) from normal operationdata. As another example, a deviation may be detected if the P and/orvibration of the compressor (or an individual impeller) exceeds aspecific absolute threshold, either as a set threshold or as a setabsolute difference from normal operation data. The data analysis may bedone over one or more different time frames, and the deviationpercentage or threshold may depend on the time frame for comparison. Inone or more embodiments, the difference from normal operation datarequired to detect a deviation over a short time frame may be relativelylarge as compared to analysis of a longer time frame, which may requirea relatively smaller difference to detect a deviation. For example, agradual but consistent increase or decrease in pressure or vibrationover a long time frame may be used in predicting long-term failure. Fordetecting fouling of impeller blades in particular, vibration data oftenfollows a predictable increase over time when monitored continuously.But such vibration data may, in some embodiments, be more effective inpredicting fouling when considered in conjunction with other data (e.g.,pressure data).

The data comparison may also be made with respect to various differentpieces of equipment. As one example, the data comparison may be limitedto only the compressor (or impeller) in question. As another example,the data comparison may be made relative to other compressors (orimpellers) in the system, and potentially to all other compressors (orimpellers) of the same type within the system. As a further example, thedata comparison may be made relative to historical data, includinghistorical data for the same compressor (or impeller) or historical datafor other compressors (or impellers). It is understood that dataanalysis does not necessarily need to be done for the purpose ofdetecting deviations, as described in greater detail below. For example,data comparison that indicates consistency with historical data for acompressor (or impeller) that exhibited fouling may be valuable inpredicting whether and when a specific degree of fouling will occur.

The data used for the comparison may also depend on the stage ofoperation of the compressor. For example, start-up or shut-down of themachine may place increased stresses on the system and may requiredifferent data comparison. Different criteria (% or threshold) fordeviation from normal operation may be applied during start-up orshut-down. Different comparison data may be used for analysis duringstart-up or shut-down as well, such as comparison to other start-up orshut-down data, rather than data from steady operation. As anotherexample, different criteria and/or comparison data may be used duringparticular environmental conditions, such as based on a particularseason or weather phenomenon.

In another example, the P and vibration data can be compared to previoustrend or pattern P or vibration data from the same or other compressors(or impellers). In this example, the overall trend or pattern of the Pand vibration data for a compressor (or impeller) may be analyzed todetermine which previous data sets match most closely to the presentdata. Once one or more similar trends in P or vibration data arematched, the matched data sets may provide useful predictive value. Forexample, matched data may be valuable in predicting whether and when aspecific degree of fouling will occur and/or which solutions may beeffectively implemented to address an actual or potential fouling issue.In particular, corrective actions that were effective for treatingissues for a past compressor or impeller with matching data may be usedwith some expectation of effectiveness.

Based on the analysis and comparison of data described herein, thecomputer system may take various actions, including corrective actions,notifications, predictions, etc. Corrective actions may include actionsto correct a present fouling condition or prophylactic actions toaddress predicted future fouling conditions. The corrective actionstaken may depend on a degree of fouling detected or predicted. Forexample, the system can recommend and/or initiate alternative processesto preserve the life of the compressor. Such alternative processes thatcan be implemented include running an online wash, adjusting processingparameters, feed type/quality, and/or upstream or downstream unitoperations, slowing the impeller speed, adding (or putting online) aguard bed that pulls nitrogen out of the feed gas, using a feed gas withless nitrogen, or a combination of such actions, or other actions. In acentrifugal compressor, some alternative processes or other correctiveactions may be taken on an individual impeller or impellers wherepossible. For example, as shown in FIG. 9B, a centrifugal compressor 310may be provided with individual injection ports 357 in front of eachimpeller 313 to permit a wash to be injected in advance of a desiredimpeller 313. An axial compressor 320 may be configured in a similarmanner. The effect of a wash may decrease for each successive impeller313 that the wash passes through, because the heat of compression canvaporize a portion of the wash fluid. Thus, the configuration of theports 357 in FIG. 9B enables the wash to be injected at a downstreamimpeller or impellers 313, bypassing upstream impellers 313 that exhibitno fouling issues. For a naphtha wash, this permits a smaller amount ofnaphtha wash to be used, which is advantageous in improving conversionof the system. For a reformate wash, this can reduce reprocessing costs.This data collection also allows multiple parties within the supplychain to share data and more quickly recognize underperformance ortroubleshoot reported field problems, thereby adjusting processoperations earlier or discovering the causes of problems and/orsolutions more quickly. Further, the system can predict a failure datefor a compressor (or an impeller) so that corrective actions can betaken. Taking corrective actions as described herein to address adetected or predicted fouling condition may permit the compressor tomaintain normal operation until an appropriate time for repair, such aswhen the system reaches a turn-around event or scheduled shutdown, inorder to avoid unscheduled shutdowns.

The above analysis and/or actions may further incorporate additionaldata gathered by additional sensors in and around the compressor and/orelsewhere in the system in other embodiments. This additional data mayinfluence determinations of potential problems and goals or mayinfluence the corrective actions that are suggested and implemented. Forexample, temperature sensors may be used in addition to or instead ofvibration sensors for detecting and predicting fouling in one or moreembodiments. As another example, the ΔP data from a suction strainer ora CFE may be used to detect or predict fouling in those components.

Additionally, the principles for detection and prediction of foulingdescribed herein may be applied to detection and prediction of foulingfor other equipment within the plant or components of such equipment.Examples of other equipment that may be suitable for use with theseprinciples include (without limitation) an FCC main air blower, an FCCwet gas compressor, an Oleflex main reactor effluent compressor, ahydrotreating recycle machine, compressors in MTO and olefin recoveryapplications, and a crude saturated gas compressor.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps illustrated in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A system comprising: a reactor; a heater; acompressor comprising one or more injection ports; one or more sensorsassociated with the compressor; a data collection platform comprising:one or more processors of the data collection platform; a communicationinterface of the data collection platform; and computer-readable memorystoring executable instructions that, when executed, cause the dataanalysis platform to: receive, from the one or more sensors associatedwith the compressor, sensor data associated with the compressor andcollected by the one or more sensors associated with the compressor; andsend the sensor data associated with the compressor and collected by theone or more sensors associated with the compressor; and a data analysisplatform comprising: one or more processors of the data analysisplatform; a communication interface of the data analysis platform; andcomputer-readable memory storing executable instructions that, whenexecuted, cause the data analysis platform to: receive the sensor dataassociated with the compressor and collected by the one or more sensorsassociated with the compressor; analyze the sensor data associated withthe compressor to determine potential fouling within the compressor; andbased on determining the potential fouling within the compressor, send acommand configured to cause an online wash via the one or more injectionports of the compressor to reduce the potential fouling within thecompressor.
 2. The system of claim 1, wherein the computer-readablememory of the data analysis platform stores executable instructionsthat, when executed, cause the data analysis platform to: analyze thesensor data associated with the compressor to determine the potentialfouling within the compressor based on increased vibration associatedwith the compressor.
 3. The system of claim 1, wherein thecomputer-readable memory of the data analysis platform stores executableinstructions that, when executed, cause the data analysis platform to:analyze the sensor data associated with the compressor to determine thepotential fouling within the compressor based on decreased pressureassociated with the compressor.
 4. The system of claim 1, wherein thecomputer-readable memory of the data analysis platform stores executableinstructions that, when executed, cause the data analysis platform to:analyze the sensor data associated with the compressor to determine thepotential fouling within the compressor based comparing current sensordata of the sensor data to past sensor data associated with thecompressor.
 5. The system of claim 1, comprising: a pressure sensor aspart of the one or more sensors associated with the compressor, whereinthe computer-readable memory of the data analysis platform storesexecutable instructions that, when executed, cause the data analysisplatform to: receive, from the pressure sensor, pressure data associatedwith the compressor, the pressure data including multiple measurementpoints per second of: maximum pressure associated with the compressor,minimum pressure associated with the compressor, and a current pressureassociated with the compressor.
 6. The system of claim 1, comprising: avibration sensor as part of the one or more sensors associated with thecompressor, wherein the computer-readable memory of the data analysisplatform stores executable instructions that, when executed, cause thedata analysis platform to: receive, from the vibration sensor, vibrationdata associated with the compressor, the vibration data includingmultiple measurement points per second of: maximum vibration associatedwith the compressor, minimum vibration associated with the compressor,and a current vibration associated with the compressor.
 7. The system ofclaim 1, comprising: an impeller; and a first pressure sensor and asecond pressure sensor as part of the one or more sensors associatedwith the compressor, the first pressure sensor configured to collectfirst pressure data associated with a first pressure upstream of theimpeller, and the second pressure sensor configured to collect secondpressure data associated with a second pressure downstream of theimpeller, wherein the computer-readable memory of the data analysisplatform stores executable instructions that, when executed, cause thedata analysis platform to: receive the first pressure data associatedwith the first pressure upstream of the impeller; receive the secondpressure data associated with the second pressure downstream of theimpeller; and compare the first pressure data associated with the firstpressure upstream of the impeller and the second pressure dataassociated with the second pressure downstream of the impeller todetermine potential fouling of the impeller.
 8. One or morenon-transitory computer-readable media storing executable instructionsthat, when executed, cause a data analysis platform to: receive sensordata associated with a compressor comprising one or more injection portsand collected by one or more sensors associated with the compressor;analyze the sensor data associated with the compressor to determinepotential fouling within the compressor; and based on determining thepotential fouling within the compressor, send a command configured tocause an online wash via the one or more injection ports of thecompressor to reduce the potential fouling within the compressor.
 9. Theone or more non-transitory computer-readable media of claim 8, storingexecutable instructions that, when executed, cause the data analysisplatform to: analyze the sensor data associated with the compressor todetermine the potential fouling within the compressor based on increasedvibration associated with the compressor.
 10. The one or morenon-transitory computer-readable media of claim 8, storing executableinstructions that, when executed, cause the data analysis platform to:analyze the sensor data associated with the compressor to determine thepotential fouling within the compressor based on decreased pressureassociated with the compressor.
 11. The one or more non-transitorycomputer-readable media of claim 8, storing executable instructionsthat, when executed, cause the data analysis platform to: analyze thesensor data associated with the compressor to determine the potentialfouling within the compressor based comparing current sensor data of thesensor data to past sensor data associated with the compressor.
 12. Theone or more non-transitory computer-readable media of claim 8, storingexecutable instructions that, when executed, cause the data analysisplatform to: receive pressure data collected by a pressure sensorassociated with the compressor, the pressure data including multiplemeasurement points per second of: maximum pressure associated with thecompressor, minimum pressure associated with the compressor, and acurrent pressure associated with the compressor.
 13. The one or morenon-transitory computer-readable media of claim 8, storing executableinstructions that, when executed, cause the data analysis platform to:receive vibration data collected by a vibration sensor associated withthe compressor, the vibration data including multiple measurement pointsper second of: maximum vibration associated with the compressor, minimumvibration associated with the compressor, and a current vibrationassociated with the compressor.
 14. The one or more non-transitorycomputer-readable media of claim 8, storing executable instructionsthat, when executed, cause the data analysis platform to: receive firstpressure data associated with a first pressure upstream of an impellerand collected by a first pressure sensor; receive second pressure dataassociated with a second pressure downstream of the impeller andcollected by a second pressure sensor; and compare the first pressuredata associated with the first pressure upstream of the impeller and thesecond pressure data associated with the second pressure downstream ofthe impeller to determine potential fouling of the impeller.
 15. Amethod comprising: receiving, by a data analysis computing device,sensor data associated with a compressor comprising one or moreinjection ports and collected by one or more sensors associated with thecompressor; analyzing, by the data analysis computing device, the sensordata associated with the compressor to determine potential foulingwithin the compressor; and based on determining the potential foulingwithin the compressor, sending, by the data analysis computing device, acommand configured to cause an online wash via the one or more injectionports of the compressor to reduce the potential fouling within thecompressor.
 16. The method of claim 15, comprising: analyzing, by thedata analysis computing device, the sensor data associated with thecompressor to determine the potential fouling within the compressorbased on increased vibration associated with the compressor anddecreased pressure associated with the compressor.
 17. The method ofclaim 15, comprising: analyzing, by the data analysis computing device,the sensor data associated with the compressor to determine thepotential fouling within the compressor based comparing current sensordata of the sensor data to past sensor data associated with thecompressor.
 18. The method of claim 15, comprising: receiving, by thedata analysis computing device, pressure data collected by a pressuresensor associated with the compressor, the pressure data includingmultiple measurement points per second of: maximum pressure associatedwith the compressor, minimum pressure associated with the compressor,and a current pressure associated with the compressor.
 19. The method ofclaim 15, comprising: receiving, by the data analysis computing device,vibration data collected by a vibration sensor associated with thecompressor, the vibration data including multiple measurement points persecond of: maximum vibration associated with the compressor, minimumvibration associated with the compressor, and a current vibrationassociated with the compressor.
 20. The method of claim 15, comprising:receiving, by the data analysis computing device, first pressure dataassociated with a first pressure upstream of an impeller and collectedby a first pressure sensor; receiving, by the data analysis computingdevice, second pressure data associated with a second pressuredownstream of the impeller and collected by a second pressure sensor;and comparing, by the data analysis computing device, the first pressuredata associated with the first pressure upstream of the impeller and thesecond pressure data associated with the second pressure downstream ofthe impeller to determine potential fouling of the impeller.